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Generalized Support Vector Quantile Regression KCI 등재

일반화 서포트벡터 분위수회귀에 대한 연구

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한국산업경영시스템학회지 (Journal of Society of Korea Industrial and Systems Engineering)
한국산업경영시스템학회 (Society of Korea Industrial and Systems Engineering)
초록

Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the є-insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within є. In most studies, the є-insensitive loss function is used symmetrically, and it is of interest to determine the value of є. In SVQR (Support Vector Quantile Regression), the asymmetry of the width of є and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of є. In this study, a new ε-insensitive loss function with p1 and p2 parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new ε-insensitive loss function can control the asymmetry of the width of є and the slope of the penalty using the parameters p1 and p2 , respectively. Moreover, the figures show that the asymmetry of the width of є and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.

목차
1. 서 론
2. 기존의 є-둔감함수와 수학모형
3. 제안하는 General Support VectorQuantile Regression Model(GSQVR)
4. 실 험
5. 결 론
References
저자
  • Dongju Lee(공주대학교 산업시스템공학과) | 이동주 Corresponding Author
  • Sujin Choi(한국폴리텍대학 스마트융합금형과) | 최수진